Domain adaptation via gamma, Weibull, and lognormal distributions for fault detection in chemical and energy processes
Bibliographic record
Abstract
Abstract The burgeoning development of supervised machine learning (ML) has led to its widespread applications in chemical and energy processes, such as fault detection. However, in some scenarios, collecting labelled data can be costly, hazardous, or impossible. Moreover, data of the same process can follow varying distributions due to changes in, for example, devices and environment, causing ML models to be ineffective. These challenges pose a domain adaptation task, necessitating the refinement of existing ML models to tackle issues from related applications. This study proposes a domain adaptation approach to address label scarcity and data distribution variation. The method has three stages: data distribution modelling (knowledge discovery), adaptation of target domain samples to source domains (knowledge transformation), and classifier ensemble for fault detection (knowledge fusion). Gamma, Weibull, and lognormal distributions are applied for data modelling and domain adaptation. The effectiveness of the method is validated on synthetic datasets and then applied to identify anomalies in coal mine pressure data and detect faults in the Tennessee Eastman (TE) process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".